Continuous Conversion of CT Kernel using Switchable CycleGAN with AdaIN

Serin Yang, Eung Yeop Kim, Jong Chul Ye

In X-ray computed tomography (CT) reconstruction, different filter kernels are used for different structures being emphasized. Since the raw sinogram data is usually removed after reconstruction, in case there are additional requirements for reconstructed images with other types of kernels that were not previously generated, the patient may need to be scanned again. Accordingly, there exists increasing demand for post-hoc image domain conversion from one kernel to another without sacrificing the image content. In this paper, we propose a novel unsupervised kernel conversion method using cycle-consistent generative adversarial network (cycleGAN) with adaptive instance normalization (AdaIN). In contrast to the existing deep learning approaches for kernel conversion, our method does not require paired dataset for training. In addition, our network can not only translate the images between two different kernels but also generate images on every interpolating path along an optimal transport between the two kernel image domains, enabling synergestic combination of the two filter kernels. Experimental results confirm the advantages of the proposed algorithm.

Knowledge Graph



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